Nothing
ad.test <-
function (..., data = NULL,
method=c("asymptotic","simulated","exact"),dist=FALSE,Nsim=10000)
{
#############################################################################
# This function "ad.test" tests whether k samples (k>1) come from a common
# continuous distribution, using the nonparametric (rank) test described in
# Scholz F.W. and Stephens M.A. (1987), K-sample Anderson-Darling Tests,
# Journal of the American Statistical Association, Vol 82, No. 399,
# pp. 918-924.
# This test is consistent against all alternatives.
# Ties are handled by using midranks, and according to the above
# reference two versions of the test statistic are returned.
# They are labeled version 1 and version 2, in the order introduced
# in the above reference.
# While the asymptotic P-value is always returned, there is the option
# to get an estimate based on Nsim simulations or an exact value based
# on the full enumeration distribution, provided method = "exact" is chosen
# and the number of full enumerations is <= the Nsim specified.
# If the latter is not the case, simulation is used with the indicated Nsim.
# These simulated or exact P-values are appropriate under the continuity
# assumption or, when ties are present, they are still appropriate
# conditionally on the tied rank pattern, provided randomization took
# place in allocating subjects to the respective samples, i.e., also
# under random sampling from a common discrete parent population.
#
#
#
# Inputs:
# ...: can either be a sequence of k (>1) sample vectors,
#
# or a list of k (>1) sample vectors,
#
# or y, g, where y contains the concatenated
# samples and g is a factor which by its levels
# identifies the samples in y,
#
# or a formula y ~ g with y and g as in previous case.
#
#
# data: data frame with variables usable in formula input, default = NULL.
#
# method: takes values "asymptotic", "simulated", or "exact".
# The value "asymptotic" causes calculation of P-values
# using the asymptotic approximation, always done.
#
# The value "simulated" causes estimation of P-values
# by randomly splitting the the pooled data into
# samples of sizes ns[1], ..., ns[k], where
# ns[i] is the size of the i-th sample vector,
# and n = ns[1] + ... + ns[k] is the pooled sample size.
# For each such random split the AD statistics are
# computed. This is repeated Nsim times and the proportions
# of simulated values >= the respective actually
# observed AD values are reported as P-value estimates.
#
# The value "exact" enumerates all n!/(ns[1]! * ... * ns[k])
# splits of the pooled sample and computes the respective
# AD statistics. The proportion of all enumerated AD statistics
# which are >= the respective actually observed AD values
# are reported as exact P-values.
#
# dist: = FALSE (default) or TRUE, TRUE causes the simulated
# or fully enumerated vectors of both AD statstics to be returned
# as null.dist1 and null.dist2.
#
# Nsim: number of simulations to perform,
# for method = "exact" to take hold, it needs to be at least
# equal the number of all possible splits of the pooled
# data into samples of sizes ns[1], ..., ns[k], where
# ns[i] is the size of the i-th sample vector.
#
# When there are NA's among the sample values they are removed,
# with a warning message indicating the number of NA's.
# It is up to the user to judge whether such removals make sense.
#
# An example:
# z1 <- c(0.824, 0.216, 0.538, 0.685)
# z2 <- c(0.448, 0.348, 0.443, 0.722)
# z3 <- c(0.403, 0.268, 0.440, 0.087)
# ad.test(z1,z2,z3,method="exact",dist=T,Nsim=100000)
# or
# ad.test(list(z1,z2,z3),method="exact",dist=T,Nsim=100000)
# which produces the output below.
#############################################################################
# Anderson-Darling k-sample test.
#
# Number of samples: 3
# Sample sizes: 4, 4, 4
# Number of ties: 0
#
# Mean of Anderson-Darling Criterion: 2
# Standard deviation of Anderson-Darling Criterion: 0.88133
#
# T.AD = ( Anderson-Darling Criterion - mean)/sigma
#
# Null Hypothesis: All samples come from a common population.
#
# AD T.AD asympt. P-value exact P-value
# version 1: 2.6367 0.72238 0.18525 0.20924
# version 2: 2.6200 0.70807 0.18819 0.21703
#
#
# Warning: At least one sample size is less than 5.
# asymptotic p-values may not be very accurate.
#
#############################################################################
# In order to get the output list, call
# ad.out <- ad.test(z1,z2,z3,method="exact",dist=T,Nsim=100000)
# then ad.out is of class ksamples and has components
# > names(ad.out)
# [1] "test.name" "k" "ns" "N" "n.ties"
# [6] "sig" "ad" "warning" "null.dist1" "null.dist2"
# [11] "method" "Nsim"
#
# where
# test.name = "Anderson-Darling"
# k = number of samples being compared
# ns = vector of the k sample sizes ns[1],...,ns[k]
# N = ns[1] + ... + ns[k] total sample size
# n.ties = number of ties in the combined set of all n observations
# sig = standard deviation of the AD statistic (for continuous population case)
# ad = 2 x 3 (or 2 x 4) matrix containing the AD statistics,
# standardized AD statistics, its asymptotic P-value,
# (and its exact or simulated P-value), for version 1 in the first row
# and for version 2 in the second row.
# warning = logical indicator, warning = TRUE indicates that at least
# one of the sample sizes is < 5.
# null.dist1 is a vector of simulated values of the AD statistic (version 1)
# or the full enumeration of such values.
# This vector is given when dist = TRUE is specified,
# otherwise null.dist1 = NULL is returned.
# null.dist2 is the corresponding vector for the 2nd AD statistic version.
# method = one of the following values: "asymptotic", "simulated", "exact"
# as it was ultimately used.
# Nsim = number of simulations used, when applicable.
#
# The class ksamples causes ad.out to be printed in a special output
# format when invoked simply as: > ad.out
# An example was shown above.
#
# Fritz Scholz, August 2012
#
#################################################################################
samples <- io(...,data = data)
method <- match.arg(method)
out <- na.remove(samples)
na.t <- out$na.total
if( na.t > 1) print(paste("\n",na.t," NAs were removed!\n\n"))
if( na.t == 1) print(paste("\n",na.t," NA was removed!\n\n"))
samples <- out$x.new
k <- length(samples)
if (k < 2) stop("Must have at least two samples.")
ns <- sapply(samples, length)
if (any(ns == 0)) stop("One or more samples have no observations.")
x <- unlist(samples)
n <- length(x)
Z.star <- sort(unique(x))
L <- length(Z.star)
if(dist == TRUE) Nsim <- min(Nsim,1e8)
# limits the size of null.dist1 and null.dist2
# whether method = "exact" or = "simulated"
ncomb <- 1
np <- n
for(i in 1:(k-1)){
ncomb <- ncomb * choose(np,ns[i])
np <- np-ns[i]
}
# it is possible that ncomb overflows to Inf
if( method == "exact" & Nsim < ncomb) {
method <- "simulated"
}
if( method == "exact" & dist == TRUE ) nrow <- ncomb
if( method == "simulated" & dist == TRUE ) nrow <- Nsim
if( method == "simulated" ) ncomb <- 1 # don't need ncomb anymore
if(method == "asymptotic"){
Nsim <- 1
dist <- FALSE
}
dist1 <- NULL
dist2 <- NULL
pv <- c(NA,NA)
getA2mat <- dist
useExact <- FALSE
if(method == "exact") useExact <- TRUE
if(getA2mat){
a2mat <- matrix(0,nrow=nrow,ncol=2)}else{
a2mat <- 0
}
ans <- numeric(2)
pval <- numeric(2)
out0 <- .C("adkTestStat0",ans=as.double(ans),k=as.integer(k),x=as.double(x),
ns=as.integer(ns),Z.star=as.double(Z.star),L=as.integer(L), PACKAGE = "kSamples")
if(method != "asymptotic"){
out1 <- .C("adkPVal0",pval=as.double(pval), Nsim=as.integer(Nsim),k=as.integer(k),
x=as.double(x),ns=as.integer(ns),
zstar=as.double(Z.star),L=as.integer(L),
useExact=as.integer(useExact),getA2mat=as.integer(getA2mat),
ncomb=as.double(ncomb),a2mat=as.double(a2mat), PACKAGE= "kSamples")
pv <- out1$pval
if(getA2mat){
a2mat <- matrix(out1$a2mat, nrow=nrow, ncol=2, byrow=FALSE,
dimnames=list(NULL, c("AkN2", "AakNk2")))
dist1 <- round(a2mat[,1],8)
dist2 <- round(a2mat[,2],8)
}
}
AkN2 <- out0[[1]][1]
AakN2 <- out0[[1]][2]
if(n > 3){
coef.d <- 0
coef.c <- 0
coef.b <- 0
coef.a <- 0
H <- sum(1/ns)
h <- sum(1/(1:(n - 1)))
g <- 0
for (i in 1:(n - 2)) {
g <- g + (1/(n - i)) * sum(1/((i + 1):(n - 1)))
}
coef.a <- (4 * g - 6) * (k - 1) + (10 - 6 * g) * H
coef.b <- (2 * g - 4) * k^2 + 8 * h * k + (2 * g - 14 * h -
4) * H - 8 * h + 4 * g - 6
coef.c <- (6 * h + 2 * g - 2) * k^2 + (4 * h - 4 * g + 6) *
k + (2 * h - 6) * H + 4 * h
coef.d <- (2 * h + 6) * k^2 - 4 * h * k
sig2 <- (coef.a * n^3 + coef.b * n^2 + coef.c * n + coef.d)/((n -
1) * (n - 2) * (n - 3))
sig <- sqrt(sig2)
TkN <- (AkN2 - (k - 1))/sig
TakN <- (AakN2 - (k - 1))/sig
pvalTkN <- ad.pval(TkN, k - 1,1)
pvalTakN <- ad.pval(TakN, k - 1,2)
}
if(n == 3 && k == 3 | n == 2){
sig <- 0
TkN <- NA
TakN <- NA
pvalTkN <- 1
pvalTakN <- 1
}
if(n == 3 && k == 2){
sig <- .3535534
TkN <- (AkN2 - (k - 1))/sig
TakN <- (AakN2 - (k - 1))/sig
pvalTkN <- ad.pval(TkN, k - 1,1)
pvalTakN <- ad.pval(TakN, k - 1,2)
}
warning <- min(ns) < 5
if(method=="asymptotic"){
ad.mat <- matrix(c(signif(AkN2,5), signif(TkN, 5),
signif(pvalTkN, 5), signif(AakN2,3) ,
signif(TakN, 5), signif(pvalTakN, 5)),
byrow = TRUE, ncol = 3)
}else{
ad.mat <- matrix(c(signif(AkN2,5), signif(TkN, 5),
signif(pvalTkN, 5), signif(pv[1],5),signif(AakN2,3) ,
signif(TakN, 5), signif(pvalTakN, 5),
signif(pv[2],5)), byrow = TRUE, ncol = 4)
}
if(method=="asymptotic"){
dimnames(ad.mat) <- list(c("version 1:","version 2:"),
c("AD","T.AD"," asympt. P-value"))
}
if(method=="exact"){
dimnames(ad.mat) <- list(c("version 1:","version 2:"),
c("AD","T.AD"," asympt. P-value"," exact P-value"))
}
if(method=="simulated"){
dimnames(ad.mat) <- list(c("version 1:","version 2:"),
c("AD","T.AD"," asympt. P-value"," sim. P-value"))
}
object <- list(test.name ="Anderson-Darling",
k = k, ns = ns, N = n, n.ties = n - L, sig = round(sig, 5),
ad = ad.mat, warning = warning, null.dist1 = dist1,
null.dist2 = dist2, method=method, Nsim=Nsim)
class(object) <- "kSamples"
object
}
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